Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations77097
Missing cells42708
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory104.0 B

Variable types

Numeric8
Categorical4
DateTime1

Alerts

DIRECTION has constant value "A" Constant
POSITION_QC has constant value "1" Constant
TIME_QC has constant value "1" Constant
LONGITUDE is highly overall correlated with PSALHigh correlation
PRES is highly overall correlated with TEMPHigh correlation
PSAL is highly overall correlated with LONGITUDEHigh correlation
TEMP is highly overall correlated with PRESHigh correlation
PSAL has 21536 (27.9%) missing values Missing
TEMP has 21172 (27.5%) missing values Missing
N_POINTS is uniformly distributed Uniform
N_POINTS has unique values Unique

Reproduction

Analysis started2025-09-04 11:41:27.847928
Analysis finished2025-09-04 11:41:47.024070
Duration19.18 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

N_POINTS
Real number (ℝ)

Uniform  Unique 

Distinct77097
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38548
Minimum0
Maximum77096
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:47.144139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3854.8
Q119274
median38548
Q357822
95-th percentile73241.2
Maximum77096
Range77096
Interquartile range (IQR)38548

Descriptive statistics

Standard deviation22256.131
Coefficient of variation (CV)0.5773615
Kurtosis-1.2
Mean38548
Median Absolute Deviation (MAD)19274
Skewness0
Sum2.9719352 × 109
Variance4.9533538 × 108
MonotonicityStrictly increasing
2025-09-04T11:41:47.298415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77096 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
77080 1
 
< 0.1%
Other values (77087) 77087
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
77096 1
< 0.1%
77095 1
< 0.1%
77094 1
< 0.1%
77093 1
< 0.1%
77092 1
< 0.1%
77091 1
< 0.1%
77090 1
< 0.1%
77089 1
< 0.1%
77088 1
< 0.1%
77087 1
< 0.1%

CYCLE_NUMBER
Real number (ℝ)

Distinct96
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.486167
Minimum3
Maximum491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:47.464884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile17
Q139
median77
Q3101
95-th percentile261
Maximum491
Range488
Interquartile range (IQR)62

Descriptive statistics

Standard deviation78.150495
Coefficient of variation (CV)0.90361844
Kurtosis11.274668
Mean86.486167
Median Absolute Deviation (MAD)29
Skewness2.9679091
Sum6667824
Variance6107.4999
MonotonicityNot monotonic
2025-09-04T11:41:47.619989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 3018
 
3.9%
81 2784
 
3.6%
76 2560
 
3.3%
49 2188
 
2.8%
83 2154
 
2.8%
108 2083
 
2.7%
38 2028
 
2.6%
51 1694
 
2.2%
50 1690
 
2.2%
78 1677
 
2.2%
Other values (86) 55221
71.6%
ValueCountFrequency (%)
3 490
 
0.6%
11 998
1.3%
12 998
1.3%
13 998
1.3%
16 185
 
0.2%
17 434
 
0.6%
18 274
 
0.4%
19 1513
2.0%
20 1243
1.6%
21 1120
1.5%
ValueCountFrequency (%)
491 687
0.9%
490 686
0.9%
347 44
 
0.1%
346 45
 
0.1%
345 44
 
0.1%
266 538
0.7%
264 538
0.7%
263 498
0.6%
262 498
0.6%
261 498
0.6%

DATA_MODE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
R
47786 
A
28007 
D
 
1304

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters77097
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 47786
62.0%
A 28007
36.3%
D 1304
 
1.7%

Length

2025-09-04T11:41:47.766044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T11:41:47.845505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
r 47786
62.0%
a 28007
36.3%
d 1304
 
1.7%

Most occurring characters

ValueCountFrequency (%)
R 47786
62.0%
A 28007
36.3%
D 1304
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 47786
62.0%
A 28007
36.3%
D 1304
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 47786
62.0%
A 28007
36.3%
D 1304
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 47786
62.0%
A 28007
36.3%
D 1304
 
1.7%

DIRECTION
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
A
77097 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters77097
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 77097
100.0%

Length

2025-09-04T11:41:47.941172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T11:41:48.007237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 77097
100.0%

Most occurring characters

ValueCountFrequency (%)
A 77097
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 77097
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 77097
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 77097
100.0%

PLATFORM_NUMBER
Real number (ℝ)

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3972396
Minimum1901897
Maximum7902287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:48.123160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1901897
5-th percentile1902286
Q11902594
median2903466
Q35907167
95-th percentile7902170
Maximum7902287
Range6000390
Interquartile range (IQR)4004573

Descriptive statistics

Standard deviation2148764.4
Coefficient of variation (CV)0.54092401
Kurtosis-1.2405307
Mean3972396
Median Absolute Deviation (MAD)1000995
Skewness0.62454748
Sum3.0625982 × 1011
Variance4.6171883 × 1012
MonotonicityNot monotonic
2025-09-04T11:41:48.270071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6990514 5568
 
7.2%
6990503 4379
 
5.7%
1902594 4178
 
5.4%
3902490 4103
 
5.3%
2903829 3173
 
4.1%
2903831 3000
 
3.9%
1902373 2117
 
2.7%
1902581 2066
 
2.7%
2903434 2018
 
2.6%
2902273 1631
 
2.1%
Other values (80) 44864
58.2%
ValueCountFrequency (%)
1901897 80
 
0.1%
1902194 996
1.3%
1902198 1494
1.9%
1902286 1494
1.9%
1902287 499
 
0.6%
1902289 1494
1.9%
1902373 2117
2.7%
1902455 999
1.3%
1902457 1487
1.9%
1902458 1485
1.9%
ValueCountFrequency (%)
7902287 185
 
0.2%
7902251 186
 
0.2%
7902249 186
 
0.2%
7902248 153
 
0.2%
7902247 186
 
0.2%
7902242 179
 
0.2%
7902200 1612
2.1%
7902170 1603
2.1%
7901136 584
 
0.8%
7901128 185
 
0.2%

POSITION_QC
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
1
77097 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters77097
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 77097
100.0%

Length

2025-09-04T11:41:48.403416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T11:41:48.466164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 77097
100.0%

Most occurring characters

ValueCountFrequency (%)
1 77097
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 77097
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 77097
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 77097
100.0%

PRES
Real number (ℝ)

High correlation 

Distinct34024
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.90783
Minimum0
Maximum999.96002
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:48.566216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.5
Q1173.5
median414.29999
Q3705.91998
95-th percentile938.20001
Maximum999.96002
Range999.96002
Interquartile range (IQR)532.41998

Descriptive statistics

Standard deviation300.49176
Coefficient of variation (CV)0.67692377
Kurtosis-1.231443
Mean443.90783
Median Absolute Deviation (MAD)261.82001
Skewness0.19813133
Sum34223962
Variance90295.297
MonotonicityNot monotonic
2025-09-04T11:41:48.719708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 50
 
0.1%
2 46
 
0.1%
1 46
 
0.1%
3 42
 
0.1%
0.2 41
 
0.1%
788 38
 
< 0.1%
9 38
 
< 0.1%
938 37
 
< 0.1%
688 37
 
< 0.1%
638 36
 
< 0.1%
Other values (34014) 76686
99.5%
ValueCountFrequency (%)
0 13
< 0.1%
0.005 4
 
< 0.1%
0.01 1
 
< 0.1%
0.032 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 30
< 0.1%
0.105 4
 
< 0.1%
0.11 3
 
< 0.1%
0.132 1
 
< 0.1%
0.19 1
 
< 0.1%
ValueCountFrequency (%)
999.960022 8
< 0.1%
999.920044 1
 
< 0.1%
999.919983 1
 
< 0.1%
999.909973 1
 
< 0.1%
999.900024 1
 
< 0.1%
999.890015 1
 
< 0.1%
999.809998 1
 
< 0.1%
999.800049 1
 
< 0.1%
999.799988 4
< 0.1%
999.77002 1
 
< 0.1%

PSAL
Real number (ℝ)

High correlation  Missing 

Distinct11332
Distinct (%)20.4%
Missing21536
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean33.520775
Minimum0.1
Maximum36.656971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:48.861603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile26.238001
Q134.987
median35.087002
Q335.416
95-th percentile36.166302
Maximum36.656971
Range36.556971
Interquartile range (IQR)0.429

Descriptive statistics

Standard deviation6.861088
Coefficient of variation (CV)0.20468167
Kurtosis17.776876
Mean33.520775
Median Absolute Deviation (MAD)0.136002
Skewness-4.3287728
Sum1862447.8
Variance47.074529
MonotonicityNot monotonic
2025-09-04T11:41:49.025503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.103 259
 
0.3%
0.102 246
 
0.3%
35.035 227
 
0.3%
0.104 209
 
0.3%
35.034 207
 
0.3%
35.028999 194
 
0.3%
35.030998 193
 
0.3%
0.105 193
 
0.3%
35.035999 189
 
0.2%
35.029999 188
 
0.2%
Other values (11322) 53456
69.3%
(Missing) 21536
27.9%
ValueCountFrequency (%)
0.1 77
 
0.1%
0.101 130
0.2%
0.102 246
0.3%
0.103 259
0.3%
0.104 209
0.3%
0.105 193
0.3%
0.106 153
0.2%
0.107 140
0.2%
0.108 105
0.1%
0.109 56
 
0.1%
ValueCountFrequency (%)
36.656971 1
 
< 0.1%
36.656872 3
< 0.1%
36.656773 4
< 0.1%
36.656673 7
< 0.1%
36.65657 5
< 0.1%
36.656471 3
< 0.1%
36.656372 1
 
< 0.1%
36.65617 1
 
< 0.1%
36.65567 1
 
< 0.1%
36.655571 1
 
< 0.1%

TEMP
Real number (ℝ)

High correlation  Missing 

Distinct22875
Distinct (%)40.9%
Missing21172
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean13.90516
Minimum5.8977
Maximum30.517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:49.198750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.8977
5-th percentile7.41068
Q19.254
median11.298
Q315.885
95-th percentile28.966999
Maximum30.517
Range24.6193
Interquartile range (IQR)6.631

Descriptive statistics

Standard deviation6.7162371
Coefficient of variation (CV)0.48300323
Kurtosis0.2579252
Mean13.90516
Median Absolute Deviation (MAD)2.557
Skewness1.2495033
Sum777646.05
Variance45.107841
MonotonicityNot monotonic
2025-09-04T11:41:49.360618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.692 19
 
< 0.1%
27.400999 18
 
< 0.1%
28.867001 18
 
< 0.1%
8.362 17
 
< 0.1%
9.906 17
 
< 0.1%
10.556 17
 
< 0.1%
7.863 16
 
< 0.1%
9.739 16
 
< 0.1%
9.632 15
 
< 0.1%
8.365 15
 
< 0.1%
Other values (22865) 55757
72.3%
(Missing) 21172
 
27.5%
ValueCountFrequency (%)
5.8977 1
< 0.1%
5.932 1
< 0.1%
5.945 1
< 0.1%
5.979 1
< 0.1%
5.999 1
< 0.1%
6.033 1
< 0.1%
6.112 1
< 0.1%
6.144 1
< 0.1%
6.154 1
< 0.1%
6.186 1
< 0.1%
ValueCountFrequency (%)
30.517 1
< 0.1%
30.513 1
< 0.1%
30.511 1
< 0.1%
30.507 1
< 0.1%
30.506001 1
< 0.1%
30.497999 1
< 0.1%
30.483999 1
< 0.1%
30.479 1
< 0.1%
30.478001 1
< 0.1%
30.476999 1
< 0.1%

TIME_QC
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
1
77097 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters77097
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 77097
100.0%

Length

2025-09-04T11:41:49.495174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-04T11:41:49.558894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 77097
100.0%

Most occurring characters

ValueCountFrequency (%)
1 77097
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 77097
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 77097
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 77097
100.0%

TIME
Date

Distinct243
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size602.4 KiB
Minimum2025-08-04 00:01:53+00:00
Maximum2025-09-03 19:32:56.002000+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-04T11:41:49.656430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:49.830833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LATITUDE
Real number (ℝ)

Distinct230
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6953572
Minimum0.049426667
Maximum19.083333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:49.990484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.049426667
5-th percentile0.1246
Q13.2724
median7.88894
Q314.733333
95-th percentile17.91853
Maximum19.083333
Range19.033907
Interquartile range (IQR)11.460933

Descriptive statistics

Standard deviation6.2369607
Coefficient of variation (CV)0.71727481
Kurtosis-1.3974934
Mean8.6953572
Median Absolute Deviation (MAD)5.93706
Skewness0.10195452
Sum670385.95
Variance38.899679
MonotonicityNot monotonic
2025-09-04T11:41:50.202886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1215726667 1478
 
1.9%
0.07818183333 1460
 
1.9%
0.7588755 1441
 
1.9%
10.00702167 1419
 
1.8%
16.68359183 1410
 
1.8%
16.45436783 1397
 
1.8%
0.4406276667 1389
 
1.8%
10.254466 1383
 
1.8%
16.45046267 1382
 
1.8%
16.524405 1379
 
1.8%
Other values (220) 62959
81.7%
ValueCountFrequency (%)
0.04942666667 686
0.9%
0.07818183333 1460
1.9%
0.1215726667 1478
1.9%
0.1246 498
 
0.6%
0.3072216667 690
0.9%
0.3354 498
 
0.6%
0.439 498
 
0.6%
0.4406276667 1389
1.8%
0.5026 499
 
0.6%
0.5686643333 1336
1.7%
ValueCountFrequency (%)
19.08333333 62
 
0.1%
19.06666667 62
 
0.1%
18.808397 511
0.7%
18.76666667 62
 
0.1%
18.719349 512
0.7%
18.67306167 686
0.9%
18.667061 499
0.6%
18.5 26
 
< 0.1%
18.48649 62
 
0.1%
18.469775 687
0.9%

LONGITUDE
Real number (ℝ)

High correlation 

Distinct233
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.272055
Minimum60.23333
Maximum89.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.4 KiB
2025-09-04T11:41:50.381421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.23333
5-th percentile62.711563
Q167.019911
median75.3714
Q385.79227
95-th percentile88.60382
Maximum89.9
Range29.66667
Interquartile range (IQR)18.772359

Descriptive statistics

Standard deviation9.2251123
Coefficient of variation (CV)0.12255693
Kurtosis-1.5121036
Mean75.272055
Median Absolute Deviation (MAD)8.5116
Skewness0.16923968
Sum5803249.6
Variance85.102698
MonotonicityNot monotonic
2025-09-04T11:41:50.534793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.45284467 1478
 
1.9%
75.94771233 1460
 
1.9%
77.14380583 1441
 
1.9%
87.08379983 1419
 
1.8%
66.8708135 1410
 
1.8%
67.01991067 1397
 
1.8%
64.83623317 1389
 
1.8%
86.93314217 1383
 
1.8%
66.98791517 1382
 
1.8%
66.91886417 1379
 
1.8%
Other values (223) 62959
81.7%
ValueCountFrequency (%)
60.23333 687
0.9%
60.72015833 686
0.9%
61.90752 504
0.7%
62.0516 504
0.7%
62.09307 504
0.7%
62.2637 40
 
0.1%
62.3167 366
0.5%
62.663177 512
0.7%
62.711563 499
0.6%
62.8079 40
 
0.1%
ValueCountFrequency (%)
89.9 62
 
0.1%
89.88333333 34
 
< 0.1%
89.8377 490
0.6%
89.81666667 124
 
0.2%
89.31666667 27
 
< 0.1%
89.07985 999
1.3%
89.01666667 61
 
0.1%
88.82374 1059
1.4%
88.65124 997
1.3%
88.60382 1058
1.4%

Interactions

2025-09-04T11:41:44.600428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:30.635100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:33.429492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:35.645994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:37.848022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.481256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.613181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:42.909204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:44.816674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:30.871176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:33.723631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:35.904397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:38.149202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.633061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.763428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:43.122558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:45.020151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:31.127395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:34.021304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:36.266314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:38.372968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.776307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.885404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:43.325362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:45.234522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:31.451724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:34.377742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:36.528266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:38.662800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.917730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:42.015647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:43.532529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:45.461625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:32.058962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:34.613379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:36.798518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:39.941130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.066126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:42.137186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:43.734110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:45.647468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:32.475563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:34.871547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:36.932519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.076956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.204479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:42.292647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:43.950743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:45.775717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:32.809257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:35.111282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:37.147993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.203982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.339718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:42.483207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:44.154186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:45.916229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:33.076819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:35.425018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:37.557298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:40.349921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:41.479778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:42.687807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-04T11:41:44.371551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-04T11:41:50.665533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CYCLE_NUMBERDATA_MODELATITUDELONGITUDEN_POINTSPLATFORM_NUMBERPRESPSALTEMP
CYCLE_NUMBER1.0000.378-0.173-0.2580.056-0.0210.0180.0300.023
DATA_MODE0.3781.0000.3500.3370.1710.3250.0430.1910.068
LATITUDE-0.1730.3501.000-0.045-0.0230.043-0.0250.3540.152
LONGITUDE-0.2580.337-0.0451.0000.002-0.068-0.052-0.764-0.124
N_POINTS0.0560.171-0.0230.0021.0000.0310.009-0.010-0.020
PLATFORM_NUMBER-0.0210.3250.043-0.0680.0311.000-0.0710.0550.109
PRES0.0180.043-0.025-0.0520.009-0.0711.000-0.149-0.957
PSAL0.0300.1910.354-0.764-0.0100.055-0.1491.0000.300
TEMP0.0230.0680.152-0.124-0.0200.109-0.9570.3001.000

Missing values

2025-09-04T11:41:46.132397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-04T11:41:46.351753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-04T11:41:46.909225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

N_POINTSCYCLE_NUMBERDATA_MODEDIRECTIONPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QCTIMELATITUDELONGITUDE
0048RA290343410.960.13529.28200012025-08-04 00:01:53+00:0012.5043186.06941
1148RA290343411.960.13529.28200012025-08-04 00:01:53+00:0012.5043186.06941
2248RA290343412.960.13529.28000112025-08-04 00:01:53+00:0012.5043186.06941
3348RA290343414.000.13529.28200012025-08-04 00:01:53+00:0012.5043186.06941
4448RA290343415.040.13529.28700112025-08-04 00:01:53+00:0012.5043186.06941
5548RA290343416.000.13529.28800012025-08-04 00:01:53+00:0012.5043186.06941
6648RA290343417.000.13529.28599912025-08-04 00:01:53+00:0012.5043186.06941
7748RA290343418.000.13529.28800012025-08-04 00:01:53+00:0012.5043186.06941
8848RA290343419.000.13529.28900012025-08-04 00:01:53+00:0012.5043186.06941
9948RA2903434110.240.13529.29000112025-08-04 00:01:53+00:0012.5043186.06941
N_POINTSCYCLE_NUMBERDATA_MODEDIRECTIONPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QCTIMELATITUDELONGITUDE
770877708751RA29034341982.0000000.1016.85512025-09-03 19:32:56.002000+00:0013.2763686.45193
770887708851RA29034341984.0000000.1016.84712025-09-03 19:32:56.002000+00:0013.2763686.45193
770897708951RA29034341986.0000000.1016.84112025-09-03 19:32:56.002000+00:0013.2763686.45193
770907709051RA29034341988.0000000.1016.83512025-09-03 19:32:56.002000+00:0013.2763686.45193
770917709151RA29034341989.9600220.1016.82912025-09-03 19:32:56.002000+00:0013.2763686.45193
770927709251RA29034341991.9600220.1016.81712025-09-03 19:32:56.002000+00:0013.2763686.45193
770937709351RA29034341994.0000000.1016.79312025-09-03 19:32:56.002000+00:0013.2763686.45193
770947709451RA29034341995.9600220.1016.78812025-09-03 19:32:56.002000+00:0013.2763686.45193
770957709551RA29034341997.9600220.1016.77612025-09-03 19:32:56.002000+00:0013.2763686.45193
770967709651RA29034341999.9600220.1016.76912025-09-03 19:32:56.002000+00:0013.2763686.45193